Introduction

Most retail questions are web search questions. Who sells this product, and at what price in each ZIP code? Where does it rank on Amazon, in Google, and in AI answers? What are reviewers complaining about? Which sellers are advertising below MAP?

Nimble answers them in three ways:

• Web Search Agents research across retailers and build cited, schema-matched datasets.

• Extraction Templates return normalized product, search, and review data from major marketplaces.

• Search finds where products are listed and discussed.

Why generic tools fall short in retail

• Incomplete: prices, stock, and seller details often sit behind filters, variant selectors, store pickers, and pagination that search results never show. Nimble browses into them.

• Stale: batch datasets miss hour-by-hour price and stock swings. Nimble retrieves the live page.

• Not local: prices and availability vary by ZIP code. Nimble returns pages as a local shopper sees them.

• Unverifiable: enforcement and pricing decisions need evidence. Nimble returns sources, timestamps, and screenshots.

Use cases

Competitive and dynamic pricing

• Track price, promotions, and availability across retailers, regions, and ZIP codes.

• Web Search Agents in dataset-building mode find every retailer carrying a product and return price and stock in your schema. Enrichment mode adds competitor prices to your own SKU list.

Digital shelf analytics

• Titles, images, bullets, ratings, review counts, badges, buy-box owner, delivery speed, and rank across Amazon, Walmart, Target, and more, using Extraction Templates.

• Surface content gaps and conversion risks; correlate with rank and sell-through.

Search and AI visibility

• Google organic, ads, and local packs, plus AI Overviews, Google AI Mode, ChatGPT, and Gemini answers for your category prompts, using Extraction Templates on a schedule, by location and device.

• See whether your brand appears in AI answers and which sources they cite.

Reviews and voice of the customer

• Collect reviews across marketplaces and Google Maps, classify themes (packaging, sizing, delivery), and alert in Slack or Teams when sentiment shifts.

• Use Web Search Agents in research mode for cited briefs on emerging product issues.

MAP and unauthorized-seller enforcement

• Hourly, ZIP-level price checks against your MAP tables across marketplaces, D2C sites, and resellers.

• Search-ad and Shopping placements advertising below MAP.

• Seller identity, storefront, and history, to identify unauthorized or gray-market sellers.

• Content violations: unauthorized bundles, misleading pack sizes, outdated images.

Evidence packs for enforcement

Artifact	Why it matters	Typical fields
Full-page screenshot	Human-verifiable proof of page state	URL, timestamp, locale/ZIP
Parsed price record	Machine-readable violation data	SKU/GTIN, seller, price, list price, promo, MAP, delta, stock
Product snapshot	Context for variant and multipack claims	Title, attributes, pack size, buy-box owner
Seller profile	Confirms unauthorized status	Seller ID and name, rating, country, storefront URL
Search capture	Shows advertised price and placement	Query, placement type, ad text, price, landing URL
History	Establishes duration and severity	First seen, last seen, occurrences per ZIP and site

Enforcement workflow: prioritize violations by MAP delta, revenue, and duration → route cases to CRM or ticketing (Salesforce, HubSpot, Jira) with the evidence attached → act (reseller outreach, marketplace report, cease-and-desist) → verify by re-checking after action.

How it fits together

Retail question	Nimble product	Output
Who sells this, at what price, where?	Web Search Agent (dataset building)	Cited, schema-matched dataset
What's on this Amazon, Walmart, or Target page?	Extraction Templates	Normalized product JSON
Where do we rank in Google and AI answers?	Extraction Templates (Google Search, AI Overviews, ChatGPT, Gemini)	Positions, citations, AI answer presence
Why did sentiment drop?	Web Search Agent (research)	Cited brief
Is anyone selling below MAP?	Templates + Web Search Agent, scheduled	Violations with evidence packs

Delivery

API responses for agents and apps, or scheduled delivery to Snowflake, Databricks, BigQuery, or S3/GCS in JSON, CSV, or Parquet. Alerts go to Slack, Teams, or your CRM. For a hands-off setup, Nimble's managed data services configure and run the entire pipeline and stream data to your stack.

Proof points

• Grips Intelligence: reliable e-commerce data across 45,000 websites and 65,000 brands. Case study

• Pricing teams: a global grocery delivery platform reported 40% less time on pricing analysis and 25% better pricing accuracy. Retail data use cases

• Extraction benchmark: 90% completeness and 99.1% fetch success across 5,856 URLs. Benchmarks

Implementation (typically under 30 days)

1. Prioritize SKUs, channels, and ZIP codes; load MAP tables if needed.

2. Define schemas for pricing, shelf, visibility, and reviews.

3. Configure Extraction Templates and Web Search Agents; set the cadence.

4. Land data in your warehouse; wire KPIs and alerts.

5. Review results and expand coverage.

Governance

SOC 2 Type II, GDPR and CCPA alignment, ethical sourcing, DPAs, and public data only. Trust Center

Get started

Start with the free tier (5,000 requests a month) or contact sales to scope a managed retail pipeline.